Skill Eval

A diagnostic instrument for AI skills. Feed it any skill, get back a structured report — what's working, what's broken (Bad Cases), and what to fix — then iterate until it passes.

Output Structure (Mandatory)

Create {target-skill-name}-eval/ at the same level as the target skill:

{target-skill-name}-eval/
├── v1/
│   ├── plan.md
│   ├── trigger-results.json
│   ├── cases.json
│   ├── execution-results.json
│   ├── report.md
│   └── optimized-skill/SKILL.md
├── v2/ ...
└── summary.md

Version rule: v1 → v2 → v3. Always create a new v{N}/ for each run.

Pipeline & Checkpoints

RULE: Each phase writes its file BEFORE the next begins. No file = not done. Go back.

Phase 0-1: Analyze & Plan

Input: Target SKILL.md Output: v{N}/plan.md

Assess structure (high/medium/low), dissect steps with operation types, design test strategy, identify risks and sandbox requirements.

Full protocol → agents/planner.md

CHECKPOINT: Write v{N}/plan.md. Verify it exists. Do NOT proceed until written.

Phase 1.5: Trigger Evaluation

Input: v{N}/plan.md + target SKILL.md (frontmatter description field) Output: v{N}/trigger-results.json

Design trigger probes (positive + negative queries) based on the skill's description, then test whether each probe correctly triggers or does not trigger the skill.

1. Design probes from the plan's Trigger Probe Strategy:

   • Positive probes (should trigger): direct requests, synonyms, multilingual variants, implicit intent
   • Negative probes (should NOT trigger): similar-sounding but out-of-scope queries, general info requests
   • Minimum: 5 positive + 5 negative probes (quick mode), 8+8 (deep mode)

2. Run probes via scripts/run_trigger_eval.py or manual testing against the platform's skill activation mechanism.

3. Record results as v{N}/trigger-results.json with precision, recall, and per-probe pass/fail.

Full script → scripts/run_trigger_eval.py

CHECKPOINT: Write v{N}/trigger-results.json. Must contain precision + recall metrics. Do NOT proceed until written.

Phase 2: Design Test Cases

Input: v{N}/plan.md + target SKILL.md Output: v{N}/cases.json

Generate per-step expected results with check_types (exact > regex > semantic). Quick mode: 4 cases. Deep mode: 8-12 cases.

Critical: Expected results written BEFORE execution. Never adjust after.

Full protocol → references/test-case-design.md JSON schema → references/schemas.md

CHECKPOINT: Write v{N}/cases.json. At least 4 cases (quick) or 8 (deep). Do NOT proceed until written.

Phase 3: Execute & Record

Input: v{N}/cases.json + target SKILL.md Output: v{N}/execution-results.json

Run each case with the skill active. Record per-step: action_taken, actual_output, tool_calls, tokens, time. Run at least 1 baseline (without skill).

Full protocol → agents/executor.md JSON schema → references/schemas.md

CHECKPOINT: Write v{N}/execution-results.json. Every case must have entries. Do NOT proceed until written.

Phase 4: Score & Verify

Input: v{N}/execution-results.json + v{N}/cases.json Output: Scored data for Phase 5

Three independent scores per step:

• Completion (0/1): Did the operation execute?
• Correctness (0/1/2): Does actual match expected?
• Execution Quality (0/1/2): Did it follow the skill's method?

Rules: Completion=0 cascades. Scores never combined. Every sub-max score needs a reason.

Full protocol → agents/judge.md Scoring definitions → references/scoring.md Rubric templates → references/rubrics.md

CHECKPOINT: All steps scored. Every step has three scores + reasons. Ready for Phase 5.

Phase 5: Report & Optimize

Input: Scored results + Advisor analysis Outputs: v{N}/report.md + v{N}/optimized-skill/SKILL.md + summary.md

Report order: Bad Cases FIRST → Overview → Step Scores → Baseline → Efficiency → Safety → Details.

If Bad Cases exist → produce optimized skill with fixes applied. If Bad Cases = 0 + stop conditions met → PASSED.

Full protocol → agents/reporter.md Root cause analysis → agents/advisor.md Visual formats → references/report-format.md

CHECKPOINT:

• v{N}/report.md exists with Bad Cases + overview + step scores
• Bad Cases > 0 → v{N}/optimized-skill/SKILL.md exists
• summary.md updated

Bad Cases

A step is a Bad Case if ANY: Completion=0, Correctness=0, Quality=0, or safety finding.

Stop Conditions (All Must Be True to PASS)

Non-Negotiables

1. Expected results before execution.
2. Low scores need reasons (expected vs actual).
3. Bad Cases shown first.
4. Three scores stay independent. Never combined.
5. Versions immutable. New v{N} for each run.
6. Every fix traces to a Bad Case.
7. Regressions are zero-tolerance.
8. Structure check before testing.
9. Baseline proves skill's value.
10. Every phase writes its file. No file = not done.

File Index

Platform Compatibility

Security

• Sandbox untrusted skills — disposable workspace, mock data, approval mode
• Skill names sanitized — no path traversal in trigger probes
• HTML reports escaped — html.escape() on all interpolated values
• Descriptions neutralized — prompt-injection patterns stripped from probe files

See agents/executor.md Safety Boundary section for full sandboxing protocol.